A novel optimized deep learning based intrusion detection framework for an IoT networks
DOI:
https://doi.org/10.22399/ijcesen.597Keywords:
Internet of Things, IoT, Intrusion Detection Systems, Long Short Term Memory, Genetic-BeeAbstract
The burgeoning importance of Internet of Things (IoT) and its diverse applications have sparked significant interest in study circles. The inherent diversity within IoT networks renders them suitable for a myriad of real-time applications, firmly embedding them into the fabric of daily life. While IoT devices streamline various activities, their susceptibility to security threats is a glaring concern. Current inadequacies in security measures render IoT networks vulnerable, presenting an enticing target for attackers. This study suggests a novel dealing to address this challenge through the execution of Intrusion Detection Systems (IDS) leveraging superior deep learning models. Inspired by the benefits of Long Short Term Memory (LSTM), we introduce the Genetic Bee LSTM(GBLSTM) networks for the development of intelligent IDS capable of detecting a wide range of cyber-attacks targeting IoT area. The methodology comprises four key execution: (i) collection of unit for profiling normal IoT device behavior, (ii) Identification of malicious devices during an attack, (iii) Prediction of attack types implemented in the network. Intensive experimentations of the suggested IDS are conducted using various validation methods and prominent metrics across different IoT threat scenarios. Moreover, comprehensive experiments are conducted to evaluate the suggested models alongside existing learning models. The results demonstrate that the GBLSTM-models outperform other intellectual models in terms of accuracy, precision, and recall, underscoring their efficacy in securing IoT networks.
References
Khan, R., Khan, S. U., Zaheer, R., & Khan, S. (2012). Future internet: The internet of things architecture, possible applications and key challenges. In 10th International Conference on Frontiers of Information Technology (FIT) (pp. 257-260). IEEE.
Simon, T. (2017). Chapter seven: Critical infrastructure and the internet of things. In Cyber Security in a Volatile World (p. 93).
E. Anthi, L. Williams and P. Burnap, (2018) Pulse: An adaptive intrusion detection for the Internet of Things. Living in the Internet of Things: Cybersecurity of the IoT - 2018, London, pp. 1-4, doi: 10.1049/cp.2018.0035.
Cybersecurity executive: Medical devices a 'bulls-eye' for cyber-attacks. (2018, February 5). Retrieved from https://www.digitalhealth.net/2017/12/medical-device-functionality-vs-cybersecurity/
Anthi, E., Javed, A., Rana, O., & Theodorakopoulos, G. (2017). Secure data sharing and analysis in cloud-based energy management systems. In Cloud Infrastructures, Services, and IoT Systems for Smart Cities (pp. 228-242). Springer.
Cyber hackers can now harm human life through smart meters. (2018, February 5). Retrieved from https://smartgridawareness.org/2014/12/30/hackers-can-now-harm-human-life/
Securing the internet of things: A proposed framework - cisco. (2018, July 13). Retrieved from https://www.cisco.com/c/en/us/about/security-center/secure-iot-proposed-framework.html
Kalpana, P., Srilatha, P., Krishna, G. S., Alkhayyat, A., & Mazumder, D. (2024). Denial of Service (DoS) Attack Detection Using Feed Forward Neural Network in Cloud Environment. In International Conference on Data Science and Network Security (ICDSNS) (pp. 1-4). https://doi.org/10.1109/ICDSNS62112.2024.10691181
Nabi, S. A., Kalpana, P., Chandra, N. S., Smitha, L., Naresh, K., Ezugwu, A. E., & Abualigah, L. (2024). Distributed private preserving learning based chaotic encryption framework for cognitive healthcare IoT systems. Informatics in Medicine Unlocked, 49, 101547. https://doi.org/10.1016/j.imu.2024.101547
Kalpana, P., & Anandan, R. (2023). A capsule attention network for plant disease classification. Traitement du Signal, 40(5), 2051-2062. https://doi.org/10.18280/ts.400523
Shanthamallu, U. S., Spanias, A., & Tepedelenlioglu, C. (2017). A brief survey of machine learning methods and their sensor and IoT
applications. In 8th International Conference on Information, Intelligence, Systems & Applications (IISA). https://doi.org/10.1109/IISA.2017.8316459
Xiao, L., Wan, X., Lu, X., Zhang, Y., & Wu, D. (2018). IoT security techniques based on machine learning. arXiv preprint arXiv:1801.06275.
Doshi, R., Apthorpe, N., & Feamster, N. (2018). Machine learning ddos detection for consumer internet of things devices. arXiv preprint arXiv:1804.04159.
Shukla, P. (2017). ML-IDS: A machine learning approach to detect wormhole attacks in internet of things. In Intelligent Systems Conference (IntelliSys) (pp. 234-240). IEEE.
Verma, A., & Ranga, V. (2019). Machine Learning Based Intrusion Detection Systems for IoT Applications. Wireless Personal Communications. 111, 2287–2310 https://doi.org/10.1007/s11277-019-06986-8
Misra, S., Krishna, P. V., Agarwal, H., Saxena, A., & Obaidat, M. S. (2011). A learning automata based solution for preventing distributed denial of service in Internet of Things. In 4th International Conference on Cyber, Physical and Social Computing (pp. 114-122).
Kasinathan, P., Costamagna, G., Khaleel, H., Pastrone, C., & Spirito, M. A. (2013). Demo: An IDS framework for Internet of Things empowered by 6lowpan. In Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security (pp. 1337-1340).
Kalpana, P., Anandan, R., Hussien, A. G., et al. (2024). Plant disease recognition using residual convolutional enlightened Swin transformer networks. Scientific Reports, 14, 8660. https://doi.org/10.1038/s41598-024-56393-8
Tama, B. A., & Rhee, K. H. (2019). An in-depth experimental study of anomaly detection using gradient boosted machine. Neural Computing and Applications, 31(4), 955-965.
Primartha, R., & Tama, B. A. (2017). Anomaly detection using random forest: A performance revisited. In International Conference on Data and Software Engineering (ICoDSE) (pp. 1-6). IEEE.
Hassan, M. M., Gumaei, A., Alsanad, A., Alrubaian, M., & Fortino, G. (2019). A Hybrid Deep Learning Model for Efficient Intrusion Detection in Big Data Environment. Information Sciences. 513;386-396 https://doi.org/10.1016/j.ins.2019.10.069
Krawczyk, B., Minku, L. L., Gama, J., Stefanowski, J., & Woźniak, M. (2017). Ensemble learning for data stream analysis: A survey. Information Fusion, 37, 132-156. https://doi.org/10.1016/j.inffus.2017.02.004
Pongle, P., & Chavan, G. (2015). Real time intrusion and wormhole attack detection in internet of things. International Journal of Computer Applications, 121(9).
Sherasiya, T., & Upadhyay, H. (2016). Intrusion Detection System for Internet of Things. International Journal of Advance Research and Innovative Ideas in Education, 2, 2344-2349.
Kasinathan, P., Pastrone, C., Spirito, M. A., & Vinkovits, M. (2013). Denial-of-service detection in 6LoWPAN based internet of things. In IEEE 9th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) (pp. 600-607).
Andrea, I., Chrysostomou, C., & Hadjichristofi, G. (2015). Internet of things: Security vulnerabilities and challenges. In IEEE Symposium on Computers and Communication (ISCC) (pp. 180-187).
Kolias, C., Kambourakis, G., Stavrou, A., & Voas, J. (2017). DDoS in the IoT: Mirai and other botnets. Computer, 50(7), 80-84. doi: 10.1109/MC.2017.201.
Xiao, L., Wan, X., Lu, X., Zhang, Y., & Wu, D. (2018). IoT security techniques based on machine learning. arXiv preprint arXiv:1801.06275.
Pahl, M.-O., & Aubet, F.-X. (2018). All eyes on you: Distributed multi-dimensional IoT microservice anomaly detection. In Proceedings of the 2018 Fourteenth International Conference on Network and Service Management (CNSM). Rome, Italy.
Poyner, I., & Sherratt, R. (2018). Privacy and security of consumer IoT devices for the pervasive monitoring of vulnerable people. DOI:10.1049/CP.2018.0043
Apthorpe, N., Reisman, D., & Feamster, N. (2016). A smart home is no castle: Privacy vulnerabilities of encrypted IoT traffic. https://doi.org/10.48550/arXiv.1705.06805
Mukherjee, A., Chakraborty, N., & Das, B. K. (2017). Whale optimization algorithm: An implementation to design low-pass FIR filter. In Innovations in Power and Advanced Computing Technologies (i-PACT).
Mukherjee, A., Chakraborty, N., & Das, B. K. (2017). Whale optimization algorithm: An implementation to design low-pass FIR filter. In Innovations in Power and Advanced Computing Technologies (i-PACT).
Supreetha, B. S., Shenoy, N., & Nayak, P. (2020). Lion Algorithm-Optimized Long Short-Term Memory Network for Groundwater Level Forecasting in Udupi District, India. Applied Computational Intelligence and Soft Computing, 2020, Article ID 8685724. https://doi.org/10.1155/2020/8685724
CIDDS-001 dataset. (2017). Retrieved November 3, 2019, from https://www.hs-coburg.de/forschungkooperation/forschungsprojekte-fentlich/ingenieurwissenschaften/cidds-coburg-intrusion-detectiondata-sets.html
NSL-KDD dataset. (2017). Retrieved November 3, 2019, from http://nsl.cs.unb.ca/nsl-kdd/
UNSW-NB15 dataset. (2017). Retrieved November 3, 2019, from https://www.unsw.adfa.edu.au/australian-centre-for-cyber-security/cybersecurity/ADFA-NB15-Datasets/
Roy, B., & Cheung, H. (2018). A Deep Learning Approach for Intrusion Detection in Internet of Things using Bi-Directional Long Short-Term Memory Recurrent Neural Networks. In International Telecommunication Conference. IEEE.
Hassan, M. M., Gumaei, A., Alsanad, A., Alrubaian, M., & Fortino, G. (2020). A Hybrid Deep Learning Model for Efficient Intrusion Detection in Big Data Environment. Information Sciences 513;386-396 https://doi.org/10.1016/j.ins.2019.10.069
Fernandez-Gago, C., Moyano, F., & Lopez, J. (2017). Modelling trust dynamics in the Internet of Things. Information Sciences, 396, 72-82. https://doi.org/10.1016/j.ins.2017.02.039
Chen, R., Guo, J., & Bao, F. (2016). Trust management for SOA-based IoT and its application to service composition. IEEE Transactions on Services Computing, 9(3), 482-495. DOI: 10.1109/TSC.2014.2365797
Saied, Y. B., Olivereau, A., Zeghlache, D., & Laurent, M. (2013). Trust management system design for the Internet of Things: A context-aware and multi-service approach. Computer Security, 39, 351-365. https://doi.org/10.1016/j.cose.2013.09.001
Ko, H. G., Ko, I. Y., & Lee, D. (2018). Multi-criteria matrix localization and integration for personalized collaborative filtering in IoT environments. Multimedia Tools and Applications, 77(4), 4697-4730.
Kalpana, P., Malleboina, K., Nikhitha, M., Saikiran, P., & Kumar, S. N. (2024). Predicting Cyberbullying on Social Media in the Big Data Era Using Machine Learning Algorithm. In International Conference on Data Science and Network Security (ICDSNS) (pp. 1-7). https://doi.org/10.1109/ICDSNS62112.2024.10691297
Asiri, S., & Miri, A. (2016). An IoT trust and reputation model based on recommender systems. In 14th Annual Conference on Privacy, Security and Trust (PST) (pp. 561-568). IEEE.
Mahmud, M., Kaiser, M. S., Rahman, M. M., Rahman, M. A., Shabut, A., Al-Mamun, S., & Hussain, A. (2018). A brain-inspired trust management model to assure security in a cloud based IoT framework for neuroscience applications. Cognitive Computing, 10(5), 864-873. https://doi.org/10.48550/arXiv.1801.03984
Zeinali, Y., & Story, B. A. (2017). Competitive probabilistic neural network. Integrated Computer-Aided Engineering, 24(2), 105-118. https://doi.org/10.3233/ICA-170540
M, P., B, J., B, B., G, S., & S, P. (2024). Energy-efficient and location-aware IoT and WSN-based precision agricultural frameworks. International Journal of Computational and Experimental Science and Engineering, 10(4);585-591. https://doi.org/10.22399/ijcesen.480
S, P., & A, P. (2024). Secured Fog-Body-Torrent : A Hybrid Symmetric Cryptography with Multi-layer Feed Forward Networks Tuned Chaotic Maps for Physiological Data Transmission in Fog-BAN Environment. International Journal of Computational and Experimental Science and Engineering, 10(4);671-681. https://doi.org/10.22399/ijcesen.490
D, jayasutha. (2024). Remote Monitoring and Early Detection of Labor Progress Using IoT-Enabled Smart Health Systems for Rural Healthcare Accessibility. International Journal of Computational and Experimental Science and Engineering, 10(4);1149-1157. https://doi.org/10.22399/ijcesen.672
S, P. S., N. R., W. B., R, R. K., & S, K. (2024). Performance Evaluation of Predicting IoT Malicious Nodes Using Machine Learning Classification Algorithms. International Journal of Computational and Experimental Science and Engineering, 10(3);341-349. https://doi.org/10.22399/ijcesen.395
Achuthankutty, S., M, P., K, D., P, K., & R, prathipa. (2024). Deep Learning Empowered Water Quality Assessment: Leveraging IoT Sensor Data with LSTM Models and Interpretability Techniques. International Journal of Computational and Experimental Science and Engineering, 10(4);731-743. https://doi.org/10.22399/ijcesen.512
Alkhatib, A., Albdor , L., Fayyad, S., & Ali, H. (2024). Blockchain-Enhanced Multi-Factor Authentication for Securing IoT Children’s Toys: Securing IoT Children’s Toys. International Journal of Computational and Experimental Science and Engineering, 10(4);1041-1049. https://doi.org/10.22399/ijcesen.417
Radhi, M., & Tahseen, I. (2024). An Enhancement for Wireless Body Area Network Using Adaptive Algorithms. International Journal of Computational and Experimental Science and Engineering, 10(3);388-396. https://doi.org/10.22399/ijcesen.409
Nagalapuram, J., & S. Samundeeswari. (2024). Genetic-Based Neural Network for Enhanced Soil Texture Analysis: Integrating Soil Sensor Data for Optimized Agricultural Management. International Journal of Computational and Experimental Science and Engineering, 10(4);962-970. https://doi.org/10.22399/ijcesen.572
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